Graphical Abstract:

Abstract:

Background: As a method to identify Differentially Expressed Genes (DEGs), Non-
Negative Matrix Factorization (NMF) has been widely praised in bioinformatics. Although NMF
can make DEGs to be easily identified, it cannot provide more associated information for these
DEGs.

Objective: The methods of network analysis can be used to analyze the correlation of genes, but
they caused more data redundancy and great complexity in gene association analysis of high dimensions.
Dimensionality reduction is worth considering in this condition.

Methods: In this paper, we provide a new framework by combining the merits of two: NMF is applied
to select DEGs for dimensionality reduction, and then Weighted Gene Co-Expression Network
Analysis (WGCNA) is introduced to cluster on DEGs into similar function modules. The
combination of NMF and WGCNA as a novel model accomplishes the analysis of DEGs for cholangiocarcinoma
(CHOL).

Results: Some hub genes from DEGs are highlighted in the co-expression network. Candidate
pathways and genes are also discovered in the most relevant module of CHOL.

Conclusion: The experiments indicate that our framework is effective and the works also provide
some useful clues to the reaches of CHOL.

Background: As a method to identify Differentially Expressed Genes (DEGs), Non-
Negative Matrix Factorization (NMF) has been widely praised in bioinformatics. Although NMF
can make DEGs to be easily identified, it cannot provide more associated information for these
DEGs.

Objective: The methods of network analysis can be used to analyze the correlation of genes, but
they caused more data redundancy and great complexity in gene association analysis of high dimensions.
Dimensionality reduction is worth considering in this condition.

Methods: In this paper, we provide a new framework by combining the merits of two: NMF is applied
to select DEGs for dimensionality reduction, and then Weighted Gene Co-Expression Network
Analysis (WGCNA) is introduced to cluster on DEGs into similar function modules. The
combination of NMF and WGCNA as a novel model accomplishes the analysis of DEGs for cholangiocarcinoma
(CHOL).

Results: Some hub genes from DEGs are highlighted in the co-expression network. Candidate
pathways and genes are also discovered in the most relevant module of CHOL.

Conclusion: The experiments indicate that our framework is effective and the works also provide
some useful clues to the reaches of CHOL.